<p>Shown here are the ROC curve Area-Under-Curve (AUC) scores, sensitivities and specificities for three classification algorithms. The values are computed using a leave-one-out cross-validation. The 95% confidence intervals are shown in brackets. The sensitivities and specificities are determined from the ROC curve, selecting in each case a threshold that gives good values for both.</p><p>Results of the machine learning analysis.</p
<p>For each subject, the ROC computed with the all-or-nothing approach is displayed. The M2 and M3 c...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
<p>Training (blue), verification (purple), and validation (red) study ROC curves are plotted with co...
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) c...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
In this paper we study techniques for generating and evaluat-ing confidence bands on ROC curves. ROC...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
<p>ROC curves for the three cases analyzed: using only images, using only neuropsychological scores ...
<p>SNS: sensitivity, SPC: specificity, ACC: accuracy, AUC: area under the curve, TRT: total response...
<p>Area Under the Curve (AUC) was obtained from the ROC curves of 9 predictors: AUC cannot be comput...
<p>The values indicated are weighted averages for the two classes under consideration; i.e. control ...
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The values indicated are weighted averages for the three classes under consideration; control, MC...
<p>For each subject, the ROC computed with the all-or-nothing approach is displayed. The M2 and M3 c...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
<p>Training (blue), verification (purple), and validation (red) study ROC curves are plotted with co...
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) c...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
In this paper we study techniques for generating and evaluat-ing confidence bands on ROC curves. ROC...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
<p>ROC curves for the three cases analyzed: using only images, using only neuropsychological scores ...
<p>SNS: sensitivity, SPC: specificity, ACC: accuracy, AUC: area under the curve, TRT: total response...
<p>Area Under the Curve (AUC) was obtained from the ROC curves of 9 predictors: AUC cannot be comput...
<p>The values indicated are weighted averages for the two classes under consideration; i.e. control ...
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The values indicated are weighted averages for the three classes under consideration; control, MC...
<p>For each subject, the ROC computed with the all-or-nothing approach is displayed. The M2 and M3 c...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
<p>Training (blue), verification (purple), and validation (red) study ROC curves are plotted with co...